Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation

Jiagang Song, Jiayu Song, Xinpan Yuan, Xiao He and Xinghui Zhu
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Jiagang Song: School of Computer Science and Engineering, Guangxi Normal University, Guiling 541004, China
Jiayu Song: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Xinpan Yuan: School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
Xiao He: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
Xinghui Zhu: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China

Future Internet, 2022, vol. 14, issue 2, 1-17

Abstract: With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and academia in recent years. One of the most widely used social network recommendation methods is collaborative filtering. However, traditional social network-based collaborative filtering algorithms will encounter problems such as low recommendation performance and cold start due to high data sparsity and uneven distribution. In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users. To this end, this paper proposes a collaborative filtering recommendation algorithm based on graphsage (GraphSAGE-CF). The algorithm first uses graphsage to learn low-dimensional feature representations of the global and local structures of user nodes in social networks and then calculates the implicit trust relationship between users through the feature representations learned by graphsage. Finally, the comprehensive evaluation shows the scores of users and implicit users on related items and predicts the scores of users on target items. Experimental results on four open standard datasets show that our proposed graphsage-cf algorithm is superior to existing algorithms in RMSE and MAE .

Keywords: GraphSAGE; graph embedding; collaborative filtering (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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